English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The evolution of the traditional power system towards the modern smart gridhas posed many new cybersecurity challenges to this critical infrastructure.One of the most dangerous cybersecurity threats is the False Data Injection(FDI) attack, especially when it is capable of completely bypassing the widelydeployed Bad Data Detector of State Estimation and interrupting the normaloperation of the power system. Most of the simulated FDI attacks are designedusing simplified linearized DC model while most industry standard StateEstimation systems are based on the nonlinear AC model. In this paper, acomprehensive FDI attack scheme is presented based on the nonlinear AC model. Acase study of the nine-bus Western System Coordinated Council (WSCC)'s powersystem is provided, using an industry standard package to assess the outcomesof the proposed design scheme. A public FDI dataset is generated as a test setfor the community to develop and evaluate new detection algorithms, which arelacking in the field. The FDI's stealthy quality of the dataset is assessed andproven through a preliminary analysis based on both physical power law andstatistical analysis.

Designing False Data Injection attacks penetrating AC-based Bad Data Detection System and FDI Dataset generation